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Published in:
2013 | OriginalPaper | Chapter
Efficient Mining of Contrast Patterns on Large Scale Imbalanced Real-Life Data
Authors : Jinjiu Li, Can Wang, Wei Wei, Mu Li, Chunming Liu
Published in: Advances in Knowledge Discovery and Data Mining
Publisher: Springer Berlin Heidelberg
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Contrast pattern mining has been studied intensively for its strong discriminative capability. However, the state-of-the-art methods rarely consider the class imbalanced problem, which has been proved to be a big challenge in mining large scale data. This paper introduces a novel pattern, i.e. converging pattern, which refers to the itemsets whose supports contrast sharply from the minority class to the majority one. A novel algorithm, ConvergMiner, which adopts T*-tree and branch bound pruning strategies to mine converging patterns efficiently, is proposed. Substantial experiments in online banking fraud detection show that the ConvergMiner greatly outperforms the existing cost-sensitive classification methods in terms of predicative accuracy. In particular, the efficiency improves with the increase of data imbalance.